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Synthetic Thyroid Disease Patient Records Dataset
Patient Health Records & Digital Health
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£179.99
About
This Synthetic Thyroid Disease Dataset is created for educational and research purposes in endocrinology, public health, and data science. It provides demographic, medical, and diagnostic details related to thyroid disease, enabling analysis of risk factors, disease progression, and treatment outcomes. The dataset can be utilized for building predictive models and exploring disease management strategies.
Dataset Features
- Age: Age of the individual in years.
- Gender: Biological sex of the individual (Male/Female).
- Smoking: Current smoking status (Yes/No).
- Hx Smoking: History of smoking (Yes/No).
- Hx Radiotherapy: History of receiving radiotherapy (Yes/No).
- Thyroid Function: Functional status of the thyroid (e.g., Euthyroid).
- Physical Examination: Findings from a physical thyroid examination (e.g., Multinodular goiter).
- Adenopathy: Presence of swollen lymph nodes (Yes/No).
- Pathology: Type of thyroid pathology identified (e.g., Papillary, Follicular).
- Focality: Whether the thyroid condition is uni-focal or multi-focal.
- Risk: Risk level associated with the condition (Low, Intermediate).
- T: Tumor size and extent (e.g., T2, T3a).
- N: Presence of lymph node involvement (e.g., N0, N1a, N1b).
- M: Indicates metastasis (e.g., M0, M1).
- Stage: Disease stage based on tumor-node-metastasis (TNM) classification (e.g., I, II, III).
- Response: Patient's response to treatment (e.g., Excellent, Structural Incomplete).
- Recurred: Indicates whether the condition has recurred (Yes/No).
Distribution
Usage
This dataset is suited for the following applications:
Risk Prediction Develop predictive models to identify patients at risk of thyroid disease recurrence or complications.
Treatment Outcome Analysis Evaluate treatment effectiveness based on response and recurrence data.
Disease Progression Modeling Study the progression of thyroid diseases using pathology, staging, and focality features.
Public Health Research Analyze demographic and clinical patterns to inform thyroid disease management strategies.
Predictive Modeling Build machine learning models to predict disease outcomes based on demographic and clinical features.
Coverage
This synthetic dataset is anonymized and adheres to data privacy standards. It is designed for research and learning purposes, with diverse cases representing varying stages, pathologies, and risk levels in thyroid disease.
License
CC0 (Public Domain)
Who Can Use It
- Data Science Practitioners: For practicing data preprocessing, classification, and regression tasks related to thyroid disease.
- Healthcare Professionals and Researchers: To explore relationships between clinical metrics and thyroid disease outcomes.
- Public Health Analysts: To understand trends and design strategies for managing thyroid diseases.
- Policy Makers and Regulators: For data-driven decision-making in thyroid disease prevention and treatment policies.
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